Governance Hurdles Slow Global GenAI Adoption, Experts Say

Concerns regarding the adoption of Generative Artificial Intelligence (GenAI) have intensified, particularly following a recent report from the Massachusetts Institute of Technology (MIT). The report highlights a troubling trend, revealing that approximately 95% of organizations are seeing no return on investments that total between $30 billion and $40 billion. This phenomenon, referred to as the “GenAI Divide,” underscores the stark contrast between various users and providers within the technology landscape.

According to Matt Carroll, CEO of Immuta, the primary barrier to GenAI’s success lies in governance rather than the technology itself. In an interview with Digital Journal, Carroll emphasized that while the hype surrounding GenAI might have diminished, its potential remains significant. The slow pace of adoption is largely due to the time required for enterprises to integrate such advanced solutions into their workflows.

Challenges in Integration and Governance

“GenAI” encompasses a wide range of capabilities that extend beyond traditional data processes. Carroll explained that technologies like Databricks and Snowflake required years for full adoption, and GenAI’s complexity—interacting with unstructured and semi-structured data—demands even greater time and investment. Enterprises often lack the necessary infrastructure to support this shift, particularly in governance.

Carroll noted, “GenAI makes everyone a data consumer, which means anyone can potentially access and act on sensitive or poor-quality data.” In most organizations, data governance teams consist of only 10 to 20 individuals, each with significant expertise. However, this small team struggles to manage access and usage decisions for tens of thousands of employees. To address this imbalance, Carroll advocates for the integration of GenAI tools into governance frameworks, effectively multiplying the team’s capabilities.

“Once that happens, broad adoption will accelerate very quickly,” Carroll stated. He believes that empowering non-technical users to operate effectively with GenAI will lead to a significant decentralization of data management, allowing organizations to realize the full benefits of the technology.

ROI and the Future of GenAI

The perception that GenAI is underperforming is often linked to failed pilots or a lack of measurable return on investment (ROI). However, Carroll argues that the issue is not with the technology itself but rather with the readiness of enterprises to utilize it effectively. For instance, Immuta is already deploying Generative Pre-trained Transformers (GPTs) for various applications, including revenue and finance, while ensuring that only high-quality, non-sensitive data is fed into the models.

He remarked, “The challenge isn’t that the models underperform; it’s that AI is outpacing the way enterprises implement data controls.” The path forward involves leveraging GenAI to enhance governance processes, which he believes is essential for delivering clear ROI.

When questioned about whether GenAI was overhyped, Carroll firmly disagreed. Instead, he pointed out that the fundamental misunderstanding lies in the timing of adoption. “Until enterprises align their data governance and infrastructure with GenAI, you won’t see adoption at scale,” he explained. Carroll anticipates that once organizations adjust their systems accordingly, they will reach a critical inflection point, leading to rapid adoption and clear returns.

As for future technologies, Carroll expressed enthusiasm for the integration of conversational AI into everyday enterprise tools. He likened this shift to the transition from Photoshop to Canva, where advanced capabilities become accessible to a wider audience. “Instead of tickets, forms, and clicks, we’ll describe what we want—data access, infrastructure, or analysis—and the system will execute,” he stated. This evolution has the potential to streamline workflows, remove bottlenecks, and democratize advanced capabilities across various sectors.

In summary, while GenAI faces significant challenges, particularly in governance, its potential impact on business processes is undeniable. With the right adjustments in data management practices, experts believe that organizations can soon unlock the transformative benefits of this technology.